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    A new approach for predicting equilibrium ratios of hydrocarbon heavy fractions: Focus on the effect of mixture composition

    Access Status
    Fulltext not available
    Authors
    Vatandoost, A.
    Khorsand Movaghar, M.
    Mousavi Dehghani, Seyed Ali
    Date
    2016
    Type
    Journal Article
    
    Metadata
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    Citation
    Vatandoost, A. and Khorsand Movaghar, M. and Mousavi Dehghani, S.A. 2016. A new approach for predicting equilibrium ratios of hydrocarbon heavy fractions: Focus on the effect of mixture composition. Flued Phase Equilibria. 410: pp. 42-55.
    Source Title
    Flued Phase Equilibria
    DOI
    10.1016/j.fluid.2015.11.024
    ISSN
    0378-3812
    School
    WASM: Minerals, Energy and Chemical Engineering (WASM-MECE)
    URI
    http://hdl.handle.net/20.500.11937/66313
    Collection
    • Curtin Research Publications
    Abstract

    © 2015 Elsevier B.V. This article introduces a new correlation for estimating equilibrium ratios (K-values) of heavy fractions which have great effects on accuracy and speed of phase equilibria calculations. The proposed correlation directly considers the effects of mixture composition and binary interaction parameters on K-values in terms of the temperature dependent term in the Peng-Robinson equation of state (PR EOS). To develop the correlation, the PR EOS is tuned for 12 crude oil samples based on laboratory data. Equilibrium ratios for heavy fractions from C 11 to C 20+ are calculated at different pressures and temperatures for each sample using the tuned PR-EOS. These equilibrium ratios are then correlated with model input parameters through a complete study of trends in K-value data. In order to examine the accuracy of proposed correlation, K-values obtained from the tuned PR EOS for two test samples are predicted using the new method and other popular correlations. The new correlation shows a better agreement with the tuned PR EOS results in comparison with methods of Whitson-Torp, Rashidi et al. and Ghafoori et al. Absolute average relative errors of predictions have been improved for up to 7% and 10% for black oil and volatile oil test sample, respectively. Sensitivity analysis of variations in K-values with respect to changes in heavy fraction properties has also been conducted to compare the behavior of empirical correlations with the PR EOS. Results indicate that the new model represent the most accurate behavior comparing to other investigated methods.

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